Following Manzan (2021), this paper examines how professional forecasters revise their uncertainty (variance) forecasts. We show that popular first moment “efficiency” tests are not applicable to study variance forecasts and instead employ monotonicity tests developed by Patton and Timmermann (2012). We find strong support for the Bayesian learning prediction of decreasing patterns in the variance of fixed-event density forecasts and their revisions as the forecast horizon declines. We explore the role of financial conditions indices in variance forecasts and document their predictive content for the revision process of US professional forecasters, although the evidence is weaker for euro area forecasters.
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